claims by tract

library(readr)
library(tidyverse)

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──

## ✓ ggplot2 3.3.5     ✓ dplyr   1.0.7
## ✓ tibble  3.1.4     ✓ stringr 1.4.0
## ✓ tidyr   1.1.3     ✓ forcats 0.5.1
## ✓ purrr   0.3.4

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(mapview)

## Warning: multiple methods tables found for 'crop'

## Warning: multiple methods tables found for 'extend'

library(sf)

## Linking to GEOS 3.8.1, GDAL 3.2.1, PROJ 7.2.1

library(tigris)

## To enable 
## caching of data, set `options(tigris_use_cache = TRUE)` in your R script or .Rprofile.

library(leaflet)
library(censusapi)

## 
## Attaching package: 'censusapi'

## The following object is masked from 'package:methods':
## 
##     getFunction

library(devtools)

## Loading required package: usethis

#claims_public <- read_csv(file="/Users/pacopoler/Desktop/s22-218z-fema/FimaNfipClaims.csv")
#claims_public <- read_csv("/Volumes/GoogleDrive/Shared drives/S22 218Z/FEMA/Data/Fema) Downloads/FimaNfipClaims.csv")
claims_public <- readRDS("claims.rds")

bay_county_names <-
  c(
    "Alameda",
    "Contra Costa",
    "Marin",
    "Napa",
    "San Francisco",
    "San Mateo",
    "Santa Clara",
    "Solano",
    "Sonoma"
  )

CA_counties <- counties("CA")

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bay_counties <- filter(CA_counties, NAME %in% bay_county_names) %>%
  rename('countyCode' = 'GEOID') %>%
  select(countyCode, NAME) %>%
  mutate(NAME = toupper(NAME)) %>%
  st_drop_geometry()


bay_public <- filter(claims_public, countyCode %in% bay_counties$countyCode) %>%
  select(censusTract, countyCode, yearOfLoss, amountPaidOnBuildingClaim) %>%
  rename('year' = 'yearOfLoss') %>%
  group_by(censusTract, year) %>%
  summarize(
    total_claims = n(),
    total_claim_value = sum(amountPaidOnBuildingClaim, na.rm = T),
    capped_claims = min(n(), 200),
    countyCode = countyCode[which.max(countyCode)]
  ) %>%
  left_join(bay_counties) %>%
  mutate(
    # NAME = (toupper(NAME) %>% paste('COUNTY', sep=' ')),
    average_claim_value = total_claim_value / total_claims
  )

## `summarise()` has grouped output by 'censusTract'. You can override using the `.groups` argument.

## Joining, by = "countyCode"

# NOW IMPORT CENSUS DATA

Sys.setenv(CENSUS_KEY="c8aa67e4086b4b5ce3a8717f59faa9a28f611dab")

pop_2020 <- tibble()

for (county in c(
  "state:06+county:097", 
  "state:06+county:075", 
  "state:06+county:041",
  "state:06+county:055",
  "state:06+county:095",
  "state:06+county:013",
  "state:06+county:085",
  "state:06+county:001",
  "state:06+county:081"
)) {
  pop_2020 <- rbind(pop_2020, (getCensus(
    name = "dec/pl",
    vintage = 2010,
    region = "tract:*", 
    regionin = county,
    # vars = "P1_001N"
    vars = "P001001"
  ) %>% 
    transmute(
      GEOID =
        paste0(state,county,tract),
      pop = P001001
    )
  )
  )
}

# bay area census visualization

tracts_2020 <- tibble()

for (county in bay_counties$NAME){
  tracts_2020 <- rbind(
    tracts_2020, 
    tracts("CA", county, year = 2010, progress_bar = F)
  )
}

pop_2020 <- pop_2020 %>% 
  left_join(
    tracts_2020 %>% 
      select(GEOID10, geometry) %>% 
      rename('GEOID' = 'GEOID10')
    ) %>% 
  st_as_sf()

## Joining, by = "GEOID"

ggplot(pop_2020) + 
  geom_sf(aes(geometry=geometry, fill = pop))

pop_pal <- colorNumeric(
  palette = "Blues",
  domain = pop_2020$pop
)

leaflet() %>% 
  addProviderTiles(providers$CartoDB.Positron) %>% 
  addPolygons(
    data = pop_2020,
    fillColor = ~pop_pal(pop),
    color = "white",
    weight = 0.5,
    fillOpacity = 0.5,
    label = ~pop
  ) %>%
  addLegend(
    position = "bottomright",
    pal = pop_pal,
    values = pop_2020$pop,
    title = "Bay Area Population - 2020"
  )

## Warning: sf layer has inconsistent datum (+proj=longlat +datum=NAD83 +no_defs).
## Need '+proj=longlat +datum=WGS84'

claims_aggregated <- group_by(bay_public, censusTract) %>%
  summarize(
    total_claims = sum(total_claims),
    total_claim_value = sum(total_claim_value)
  ) %>%
  left_join((pop_2020 %>% rename('censusTract' = 'GEOID'))) %>%
  mutate(
    claim_density = total_claims / pop,
    claim_value_density = total_claim_value / pop
  )%>% 
  st_as_sf()

## Joining, by = "censusTract"

ggplot(claims_aggregated %>% filter(total_claims < 400)) + 
  geom_sf(aes(geometry=geometry, fill = total_claims), color=NA)

#map<-claims_aggregated %>% filter(total_claims < 400)

agg_pal <- colorNumeric(
  palette = "plasma",
  domain = claims_aggregated$total_claims
)

leaflet() %>% 
  addProviderTiles(providers$CartoDB.Positron) %>% 
  addPolygons(
    data = claims_aggregated,
    fillColor = ~agg_pal(total_claims),
    color = "white",
    opacity = 0.5,
    fillOpacity = 0.5,
    weight = 1,
    label = ~total_claims
  ) %>%
  addLegend(
    position = "bottomright",
    pal = agg_pal,
    values = claims_aggregated$total_claims,
    title = "Aggregated Claims 2020"
  )

## Warning: sf layer has inconsistent datum (+proj=longlat +datum=NAD83 +no_defs).
## Need '+proj=longlat +datum=WGS84'

# POLIICES
policies_bay <- read_csv("/Volumes/GoogleDrive/Shared drives/S22 218Z/FEMA/Data/Fema) Downloads/fema_policies_bay.rds")
#policies_bay <- readRDS("/Users/pacopoler/Desktop/s22-218z-fema/fema_policies_bay.rds")

policies_by_tract <- group_by(policies_bay, censusTract) %>%
  summarize(
    policy_count = n()
  )

claims_aggregated <- claims_aggregated %>%
  left_join(policies_by_tract) %>%
  mutate(
    log_claims_per_policy = log(total_claims / policy_count),
    claims_per_policy = total_claims / policy_count,
    policy_coverage_pct = policy_count / pop * 100
  )

ggplot(claims_aggregated) + 
  geom_sf(aes(geometry=geometry, fill = policy_coverage_pct), color=NA)


# OUTCOME 1: claims by county by year

claims_by_year <- group_by(bay_public, censusTract, year) %>%
  summarize(
    total_claims = sum(total_claims),
    total_claim_value = sum(total_claim_value)
  ) %>%
  left_join(pop_2020) %>%
  mutate(
    claim_density = total_claims / pop,
    claim_value_density = total_claim_value / pop
  )

ggplot(claims_by_year) +
  geom_point(aes(x=year, y=claim_density)) +
  ylab('total number of claims per capita')

ggplot(county_data) +
  geom_histogram(aes(x=average_claim_value), bins=60) +
  ggtitle(county) +
  ylab('number of census tracts') +
  xlab('average claim value')

# OUTCOME 2: policy coverage

years <- paste('01/01', (2010:2021), sep='/') %>%
  as.Date("%m/%d/%Y")

policy_coverage_by_year <- group_by(policies_bay, 'censusTract') %>%

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